Natural products have been used by the human civilization for thousands of years. Their medicinal values have been recorded throughout history. Since the advancement of pharmacology, clinical pharmacology, pharmacognosy and analytical chemistry, the active components in a natural substance were beginning to be unveiled. A good example is the discovery of acetylsalicylic acid in willow bark. Bayer has recently celebrated the 100th anniversary of Aspirin, a purified form of acetylsalicylic acid.
There are two streams of natural product research. Since the dawn of modern pharmaceutical sciences, there has been an insatiable quest for the isolation and purification of a single active component in a natural substance. In fact, more than 60% of the pharmaceuticals which have been developed for treating cancer, hypertension and migraine are either natural in origin or natural product mimics (Newman et al., 2003). Although combinatorial techniques have succeeded as methods of optimizing structures, no de novo combinatorial compound approved as a drug has been identified on or before 2002. In hope of finding new core chemical structures, efforts are still being spent in natural product research.
Natural remedies are often composed of one or more herbs. Each herb has multiple active components. The identification, purification, activity determination, using known pharmacological models for a complex mixture, has been a monumental task. The complexity of this area of research has been the major obstacle in natural medicine development (Williamson, 2001). In his review, Liu and Yang (2006) commented that identifying active components in traditional Chinese Medicine (TCM) is the most important issue in the development of TCM. The active components could be active metabolites of the principle components of the preparation. For example, ginsenosides are major components responsible for the efficacy of ginseng. However, the activity of these ginsenosides is low and their bioavailability after oral administration is minuscule. The metabolic products, protopanaxadiols and protopanaxatriols are easily absorbed and pharmacologically active (Hasegawa, 2004). Although it is important to understand the pharmacokinetic and pharmacodynamic nature of the active components in TCM, there was no suggestion for sorting out the complicated interrelationships between potential pharmacokinetic and pharmacodynamic interactions.
The study of active ingredients in natural substances has been rather primitive in pharmaceutical sciences terms. The approach is stagnated at the discovery stage of pharmaceutical development. The general approach is to employ activity guided extraction to identify targets that have in vitro activities. This approach is extremely unsuitable for the development of nature products. For the longest time, Panax ginseng was thought to be an expensive “junk” because it has no apparent active ingredients. It was not until Hasegawa (2004) reported that the inactive ginsenosides of Panax ginseng were acting like prodrugs, when metabolized by intestinal flora release the aglycones, which have physiological activity. Rutin, a flavonoid glycoside, which is present in ginkgo and a number of other herbs, has been shown to be a potent antioxidant in vitro. However, it is difficult to substantiate the actual in vivo activity of rutin, simply because this substance is not detected in the blood stream (Hollman et al., 1997). A major component of Chuanxiong, z-ligustilide, has been shown to be a major active component of the herb; however, the bioavailability of this component is less than 3% (Yan et al., 2008). It is quite obvious that there will not be enough ligustilide reaching the site of action to exert its activity. These examples clearly show the shortcoming of using the classical pharmaceutical approach of identifying actives in an herbal preparation. The natural prodrugs, like that of ginsenosides, will be missed and actives like rutin will be pursued. In pharmaceutical science terms, compounds like ligustilide lacks drug-like properties for oral administration. Drug-like properties are basically pharmacokinetic properties of a substance which, after administration, has the ability to be absorbed in a substantial amount without being metabolized, and to be distributed via the blood stream to the site of action in sufficient quantity before being eliminated from the body. It is no surprise that drug-like properties have not been a major component of natural product research because it is new to the pharmaceutical development. Since there are permutations in arriving at the actives of an herbal extract, the complexity of delineating pharmacokinetic profiles for multi-components does appear to be prohibitive.
Recognizing the complex nature of herbal product development, Homma et al. (1992) proposed a strategy to discover biologically active components in an herbal product. The premise of the strategy is that ingredients and/or their metabolites have to be absorbed before they can exert their biological effects. Contents in plasma and urine after product administration were measured. This approach has been employed by Pan and Cheng (2006) to evaluate a Chinese herbal product, Shuangdan. It was proposed that some of the components that were present in plasma could be used for standardization of the product. This approach can certainly be used to identify absorbable components and their metabolites. Zhang et al. (2005) examined using chemical and metabolic fingerprinting for identifying potentially active ingredients in Danshen injection batches.
The advance of analytical technology may complicate this approach because the nature of components present in plasma will be different from that of the product and the number of components present could exceed that of the product because the number of potential metabolites formed could be daunting. One could argue that only the major components needed be standardized; however, this assumption is clearly flawed because potent components present in minute quantities may be missed. Among other shortcomings, this approach to discover biologically active components does not permit optimization of ratio and dosage of biologically active components.
In recent years, interests in performing pharmacological and pharmacokinetic studies on natural substances such as St. John's Wort (Schulz et al., 2005) and Ginkgo (Kwak et al., 2002; Ahlemeyer and Krieglstein, 2003) are increasing. There is no lack of publications in the area of herb-drug interactions (Brazier and Levine, 2003; Hu et al., 2005; Williamson, 2005), herbal effects on drug metabolizing enzymes (Venkataramanan et al., 2000; Mathews et al., 2002; Komoroski et al., 2004; Yim et al., 2004; Chang et al., 2006) and pharmacokinetics of active ingredients of herbs (Mathews et al., 2005; Zhou et al., 2005; Yan et al., 2007). The latter is limited to a single component. There are studies which attempted to predict in vivo herb-drug interaction using in vitro methodologies (Williamson, 2001; Mohutsky et al., 2006; Venkataramanan et al., 2006). These studies met with partial success and the general conclusion is that an in vivo study is required to confirm the results.
It has been frequently postulated that the advantage of alternative therapy is the relatively low dosage required for the treatment of an ailment (Williamson, 2001). Active components could act either additively, synergistically or antagonistically. This subject remains elusive to scientists working on the development of herbal medicine. Wang et al. (2006) have designed a method called Quantitative Composition-activity Relationship (QCAR) to identify herbs that are active in a multiple herb formula. While individual herbs contain mixtures of compounds, there was no attempt to address the effects of potential variability within each herb on the pharmacological outcome of the formula. Although in vivo interaction between herbs was reported, there were no indications as to which components in each herb were involved. The same group of scientists have also published a method to address the issue faced with mixtures in QCAR (Cheng et al., 2006). However, the active components identified using these methodologies were restricted to activity only; there was no attempt to investigate the “drug-like” properties of active components. Since a large number of herbs contain ingredients that behave like precursors, e.g., ginsenosides from Panax ginseng. In their native forms, they are inactive. This method would have missed this category of “active” ingredients. In the absence of an understanding of the number of components/precursors involved and their respective drug-like properties, it would be close to impossible to determine these intricate interactions in the body. The methods developed by this group of scientists were based on linear models. This limitation has restricted the evaluation of interactions, including synergism and antagonism. Furthermore, they do not take the nonlinear relationship between intensity of activity and concentration into account, a relationship that is important for understanding optimal dosing and degree of component-component interaction (Chou, 2006).
Pharmaceutical technologies for drug discovery have not been employed extensively in the development of natural products. There are a number of in vitro microsomal or hepatocyte studies reported for evaluating herb-drug interactions (Hu et al., 2005; Williamson, 2005; Venkataramanan et al., 2006) and metabolism of active components (Komoroski et al., 2005). However, there is no study on using physiologically based pharmacokinetic and/or pharmacodynamic models to predict the time course of active ingredients of an herbal extract in the body, nor are there any studies using the same approach to quantify the time course of a response. No in silico methods to-date employed for drug discovery have been applied to predict pharmacokinetic and pharmacodynamic interaction of active components and their metabolites after administration of an herbal extract.
There are a number of patents filed in the last 20 years outlining methods for standardizing natural products. The most advanced ones are that of Paracelsian's BioFit® (Blumenthal and Milot, 2004), CV Technologies' ChemBioPrint® (Pang et al., 2000) and PharmaPrint Inc's. PharmaPrint® technologies (Khwaja and Friedman, 2000; Khwaja and Friedman, 2002). The later two utilize bioassays involving concentrating fractions that are pharmacologically active and one or more markers are standardized along with desired activities. When both conditions are satisfied, the batch is accepted. PharmaPrint® rates these extracts pharmaceutical grade. They have used this technology to produce standardized herbs such as St. John's Wort (Khwaja and Friedman, 2000). ChemBioPrint® appears to be a bit more involved in that in addition to the in vitro assays, in vivo assays are also incorporated in the standardization procedures. Neither of these two standardization procedures directly links the activity with the putative standardized ingredients. Therefore, it is not known whether the standardized ingredients are of the right amount or the appropriate ratios. There is also no information on active ingredients that are not identified. It is well known that some of these ingredients are inactive in vitro, but they have biological activities in vivo (Hasegawa, 2004). The reason is that some of these ingredients are not actually absorbed; therefore lacking “drug-like” properties. Paracelsian's BioFit® technology claimed that an absorption assessment using Caco-2 cells were performed on the active components. However, Caco-2 has shortcomings in predicting large molecule absorption because these molecules are not permeable through the Caco-2 membrane. A significant percentage of natural ingredients have large molecular weights. The absorption of these molecules such as polysaccharides, glycosides, etc. is difficult to estimate using Caco-2 cells.
Kinetana's SimBioDAS® technology (Tam and Anderson, 2000) appears to overcome the problems that Caco-2 technology faces (Blumenthal and Milot, 2004). This technology has been employed to measure absorbable components which are active in vitro. This technology, however, has two problems: 1. it does not provide an estimate of the pharmacokinetics of ingredients and therefore, concentration-time profiles at the site of action; and 2. the cell membranes are susceptible to rupture when they are incubated with certain herbal extracts such as St. John's Wort.
There was a news release in January 2008 by an Indian firm Avesthagen announcing a new technology, MetaGrid, for the standardization of multi-constituent plant-based extracts. This technology is based on matching retention times of active components analyzed using an analytical method. While the technology may be useful for standardizing active components, however, these so-called active components have not been subjected to vigorous testing for in vivo testing. In other words this technology does not provide information on the “drug-like” properties of these components.
In short, there is no method available to adequately mine the physiologically active components of an herbal substance. It is generally believed that the activity of phytomedicine is mediated by a large number of active ingredients, each of which constitutes a relatively low quantity compared to those used in Western medicines. Furthermore, each ingredient, if given individually, would require a much higher dose to achieve the same physiological effect. It is believed, however, (while rarely demonstrated directly by experiment) that these individual ingredients, when taken together, may mutually reinforce each other synergistically. For example, in a given herbal extract (e.g. Echinacea or Ginkgo biloba), there could be several hundred chemical entities, dozens of which are active compounds and a subset of these can strongly interact with each other synergistically or by mutual inhibition. However, existing technology does not allow stringent quality control because there have been no success in elucidating the activity of these ingredients as a group. In this invention, a platform technology, which is based on formulating a mathematically rigorous procedure of describing these interactions through a combination of in vitro and in silico modelling and data analysis resulting in reverse engineering of the process and then designing an optimal composition in order to yield the most efficacious multi-component formulation, is described. The advantage of this approach is that there is no requirement to study the components individually. As a result, separation, isolation and purification of active components are not necessary; therefore, saving time and resources. FIG. 1 illustrates a model which is used to describe the concentration and effect time course of a single component and this same model can be used for describing the time course of multiple components after incorporating the mathematical procedures described herein.